Reliability Evaluation of Slope in Spatially Variable Soils Using Sliced Inverse Regression‑Based Extreme Gradient Boosting
Publication: Geo-Risk 2023
ABSTRACT
Two key challenges facing the reliability analysis of slopes considering spatial variability of geotechnical parameters are high dimensionality and computational cost. To this end, this study proposes a slice inverse regression (SIR) based Extreme gradient boosting (XGBoost) method for reliability analysis of slopes considering spatial variability. This paper uses a typical slope as an example to illustrate the proposed method. The results show that the reliability analysis based on SIR and XGBoost can predict the failure probability of a slope with reasonable accuracy and efficiency. The results are compared with those obtained by other methods. The stability and generalization of the limit gradient boosting model with sliced inverse regression in terms of computational efficiency are further illustrated.
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Geo-Risk 2023
Pages: 11 - 22
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Published online: Jul 20, 2023
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